ArticleStatistics in medicine2021
Selecting the model for multiple imputation of missing data: Just use an IC!
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Missing data considerations for patient reported outcome measures in randomized controlled trials.Journal of patient-reported outcomes · 2026Article
- Autism outcomes and neurobehavioural markers in young children born to mothers with HIV in Kenya: a protocol for the Alama project.BMJ open · 2026Article
- Radiomics of Pericoronary Adipose Tissue and CT-FFR to Predict Major Adverse Cardiovascular Events in Patients with T2DM Complicated by CAD.Journal of cardiovascular translational research · 2026Article
- Development of a machine learning-based multicenter study for predicting severe intraventricular hemorrhage in preterm infants using perinatal variables.Frontiers in neurology · 2026Observational
- Development and validation of a machine learning model for predicting preoperative deep vein thrombosis in elderly hip fracture patients.Frontiers in medicine · 2026Article
- Development of the Neutrophil-to-Platelet Ratio (NPR) Integrated with Machine Learning for Predicting Early Mortality After Mechanical Thrombectomy in Acute Ischemic Stroke.Journal of inflammation research · 2026Article
- A nomogram for predicting the risk of persistent coronary artery aneurysms in children with Kawasaki disease: a retrospective study.Frontiers in cardiovascular medicine · 2026Article
- Association between pregabalin use and delirium in critically ill patients aged 60 and older: a retrospective analysis of the MIMIC-IV database.Open medicine (Warsaw, Poland) · 2026Article
- Development and validation of a risk prediction model for autologous arteriovenous fistula thrombosis in patients receiving maintenance hemodialysis.Renal failure · 2025Article
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.BMC nephrology · 2025Article
- Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.BMC psychiatry · 2025Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Enhancing representativeness in population-based surveys to improve data quality and decision-making.Scientific reports · 2025Article
- Multiple imputation for systematically missing effect modifiers in individual participant data meta-analysis.Statistical methods in medical research · 2025Article
- Association of anaesthesia type with one-year mortality after surgery in elderly patients: a secondary retrospective cohort study.BMC anesthesiology · 2025Article
- Incorporation of missing indicator with multiple imputation in propensity score analysis with partially observed covariates: A simulation study.Statistical methods in medical research · 2025Article
- Association between lactate-to-albumin ratio and shortand long-term mortality in critically ill patients with ischemic stroke: A retrospective analysis of the MIMIC-IV database.Journal of medical biochemistry · 2025Article
- Methods for diagnosing malnutrition in patients with esophageal cancer, and the association with nutritional and inflammatory indices: A cross‑sectional study.Oncology letters · 2025Article
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4 authors.
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Abstract
Multiple imputation and maximum likelihood estimation (via the expectation-maximization algorithm) are two well-known methods readily used for analyzing data with missing values. While these two methods are often considered as being distinct from one another, multiple imputation (when using improper imputation) is actually equivalent to a stochastic expectation-maximization approximation to the likelihood. In this article, we exploit this key result to show that familiar likelihood-based approaches to model selection, such as Akaike's information criterion (AIC) and the Bayesian information criterion (BIC), can be used to choose the imputation model that best fits the observed data. Poor choice of imputation model is known to bias inference, and while sensitivity analysis has often been used to explore the implications of different imputation models, we show that the data can be used to choose an appropriate imputation model via conventional model selection tools. We show that BIC can be consistent for selecting the correct imputation model in the presence of missing data. We verify these results empirically through simulation studies, and demonstrate their practicality on two classical missing data examples. An interesting result we saw in simulations was that not only can parameter estimates be biased by misspecifying the imputation model, but also by overfitting the imputation model. This emphasizes the importance of using model selection not just to choose the appropriate type of imputation model, but also to decide on the appropriate level of imputation model complexity.
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